Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/matplotlib/font_manager.py:280: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  'Matplotlib is building the font cache using fc-list. '
Out[2]:
<matplotlib.image.AxesImage at 0x7fc790e81278>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fc790cfd8d0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.3.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, shape=(None, image_width, image_height, image_channels), name='InputReal') 
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='Input_Z')
    
    learning_rate = tf.placeholder(tf.float32, name='LearnRate')
    
    return (inputs_real, inputs_z, learning_rate)




"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/runpy.py", line 193, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>\n    app.launch_new_instance()', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 477, in start\n    ioloop.IOLoop.instance().start()', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/zmq/eventloop/ioloop.py", line 177, in start\n    super(ZMQIOLoop, self).start()', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events\n    self._handle_recv()', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback\n    callback(*args, **kwargs)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 399, in execute_request\n    user_expressions, allow_stdin)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2808, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-5-d0df65ec00a5>", line 26, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/media/dlnd/dlnd_project_facedetect_2/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/media/dlnd/dlnd_project_facedetect_2/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/media/dlnd/dlnd_project_facedetect_2/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/media/dlnd/dlnd_project_facedetect_2/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 175, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 144, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/home/ubuntu/anaconda3/envs/face_detect/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 101, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope ('discriminator', reuse = reuse):
        FirstLayer = tf.layers.conv2d (images, 32,5,2, 'SAME')
        lrelu_1 = tf.maximum(alpha*FirstLayer, FirstLayer)
        
        SecondLayer = tf.layers.conv2d (lrelu_1, 64,5,2, 'SAME')
        BatchNorm_1 = tf.layers.batch_normalization (SecondLayer, training = True)
        lrelu_2 = tf.maximum(alpha*BatchNorm_1, BatchNorm_1)
        
        ThirdLayer = tf.layers.conv2d (lrelu_2, 128,5,2, 'SAME')
        BatchNorm_2 = tf.layers.batch_normalization (ThirdLayer, training = True)
        lrelu_3 = tf.maximum(alpha*BatchNorm_2, BatchNorm_2)
        
        ReShaped = tf.reshape(lrelu_3, (-1, 4*4*128))
        
        tensor_logits = tf.layers.dense (ReShaped, 1)
        
        tensor_out = tf.sigmoid (tensor_logits)
        
        return (tensor_out, tensor_logits)
    
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope ('generator', reuse = (not is_train)):
        FirstLayer = tf.layers.dense (z, 7*7*128)
        FirstLayer = tf.reshape (FirstLayer, (-1,7,7,128))
        lrelu_1 = tf.maximum (FirstLayer*alpha, FirstLayer)
        
        SecondLayer = tf.layers.conv2d_transpose (lrelu_1, 64,5,1, 'SAME')
        BatchNorm_1 = tf.layers.batch_normalization (SecondLayer, training = is_train)
        lrelu_2 = tf.maximum (BatchNorm_1*alpha, BatchNorm_1)
        
        ThirdLayer = tf.layers.conv2d_transpose (lrelu_2, 32,5,2, 'SAME')
        BatchNorm_2 = tf.layers.batch_normalization (ThirdLayer, training = is_train)
        lrelu_3 = tf.maximum (BatchNorm_2*alpha, BatchNorm_2)
        
        logits = tf.layers.conv2d_transpose (lrelu_3, out_channel_dim, 6,2, 'SAME')
        
        output = tf.tanh (logits)
        
        return (output)
        
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return (d_loss, g_loss)
    #return None, None


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt
    


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, learn_rate = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
    
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images *= 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                _ = sess.run([d_train_opt, g_train_opt], feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
                if steps % 100 == 0:
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_real: batch_images, input_z: batch_z})
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [27]:
batch_size = 64
z_dim = 200
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.7095... Generator Loss: 1.7161
Epoch 1/2... Discriminator Loss: 1.4122... Generator Loss: 0.5618
Epoch 1/2... Discriminator Loss: 0.8895... Generator Loss: 1.8729
Epoch 1/2... Discriminator Loss: 1.1707... Generator Loss: 0.5931
Epoch 1/2... Discriminator Loss: 1.4218... Generator Loss: 0.4862
Epoch 1/2... Discriminator Loss: 1.2301... Generator Loss: 0.6267
Epoch 1/2... Discriminator Loss: 1.9063... Generator Loss: 0.2487
Epoch 1/2... Discriminator Loss: 1.0350... Generator Loss: 0.7936
Epoch 1/2... Discriminator Loss: 0.7751... Generator Loss: 0.9186
Epoch 2/2... Discriminator Loss: 2.1229... Generator Loss: 0.2153
Epoch 2/2... Discriminator Loss: 1.9719... Generator Loss: 0.2133
Epoch 2/2... Discriminator Loss: 1.0725... Generator Loss: 0.5835
Epoch 2/2... Discriminator Loss: 0.6356... Generator Loss: 1.0396
Epoch 2/2... Discriminator Loss: 1.0171... Generator Loss: 2.3914
Epoch 2/2... Discriminator Loss: 1.4801... Generator Loss: 0.3975
Epoch 2/2... Discriminator Loss: 1.2876... Generator Loss: 0.4635
Epoch 2/2... Discriminator Loss: 0.7447... Generator Loss: 0.9696
Epoch 2/2... Discriminator Loss: 0.9296... Generator Loss: 0.8210

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [30]:
batch_size = 16
z_dim = 200
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 0.2359... Generator Loss: 1.8567
Epoch 1/1... Discriminator Loss: 0.1667... Generator Loss: 2.5342
Epoch 1/1... Discriminator Loss: 0.4636... Generator Loss: 1.3495
Epoch 1/1... Discriminator Loss: 0.2104... Generator Loss: 2.1778
Epoch 1/1... Discriminator Loss: 0.3959... Generator Loss: 3.7166
Epoch 1/1... Discriminator Loss: 0.4042... Generator Loss: 1.5963
Epoch 1/1... Discriminator Loss: 0.4705... Generator Loss: 1.1800
Epoch 1/1... Discriminator Loss: 0.3538... Generator Loss: 1.6753
Epoch 1/1... Discriminator Loss: 0.4577... Generator Loss: 2.2488
Epoch 1/1... Discriminator Loss: 0.3711... Generator Loss: 1.8099
Epoch 1/1... Discriminator Loss: 0.3630... Generator Loss: 1.6481
Epoch 1/1... Discriminator Loss: 0.6240... Generator Loss: 1.1131
Epoch 1/1... Discriminator Loss: 0.8870... Generator Loss: 0.7917
Epoch 1/1... Discriminator Loss: 0.5124... Generator Loss: 1.3092
Epoch 1/1... Discriminator Loss: 0.6017... Generator Loss: 1.5188
Epoch 1/1... Discriminator Loss: 0.5479... Generator Loss: 1.3625
Epoch 1/1... Discriminator Loss: 0.9591... Generator Loss: 0.6830
Epoch 1/1... Discriminator Loss: 0.7376... Generator Loss: 0.8825
Epoch 1/1... Discriminator Loss: 0.7165... Generator Loss: 1.0238
Epoch 1/1... Discriminator Loss: 0.4699... Generator Loss: 1.5928
Epoch 1/1... Discriminator Loss: 0.7636... Generator Loss: 1.0835
Epoch 1/1... Discriminator Loss: 0.8777... Generator Loss: 0.8832
Epoch 1/1... Discriminator Loss: 0.8567... Generator Loss: 0.9479
Epoch 1/1... Discriminator Loss: 0.7958... Generator Loss: 1.2221
Epoch 1/1... Discriminator Loss: 0.7624... Generator Loss: 1.0668
Epoch 1/1... Discriminator Loss: 0.6905... Generator Loss: 1.0094
Epoch 1/1... Discriminator Loss: 0.8476... Generator Loss: 1.2483
Epoch 1/1... Discriminator Loss: 0.9332... Generator Loss: 0.8235
Epoch 1/1... Discriminator Loss: 0.9413... Generator Loss: 1.3349
Epoch 1/1... Discriminator Loss: 1.0352... Generator Loss: 0.6636
Epoch 1/1... Discriminator Loss: 0.6956... Generator Loss: 1.3126
Epoch 1/1... Discriminator Loss: 0.7505... Generator Loss: 1.0339
Epoch 1/1... Discriminator Loss: 1.0904... Generator Loss: 0.7192
Epoch 1/1... Discriminator Loss: 1.0456... Generator Loss: 0.7689
Epoch 1/1... Discriminator Loss: 0.8049... Generator Loss: 1.0651
Epoch 1/1... Discriminator Loss: 1.1448... Generator Loss: 0.6204
Epoch 1/1... Discriminator Loss: 0.8590... Generator Loss: 0.9704
Epoch 1/1... Discriminator Loss: 1.2027... Generator Loss: 0.5386
Epoch 1/1... Discriminator Loss: 0.8397... Generator Loss: 0.9304
Epoch 1/1... Discriminator Loss: 1.5394... Generator Loss: 0.3441
Epoch 1/1... Discriminator Loss: 1.0755... Generator Loss: 0.7724
Epoch 1/1... Discriminator Loss: 0.7439... Generator Loss: 1.2723
Epoch 1/1... Discriminator Loss: 1.0752... Generator Loss: 0.6252
Epoch 1/1... Discriminator Loss: 0.8160... Generator Loss: 1.1401
Epoch 1/1... Discriminator Loss: 0.7405... Generator Loss: 1.2160
Epoch 1/1... Discriminator Loss: 1.0494... Generator Loss: 0.6995
Epoch 1/1... Discriminator Loss: 0.8197... Generator Loss: 0.9740
Epoch 1/1... Discriminator Loss: 0.9249... Generator Loss: 1.0934
Epoch 1/1... Discriminator Loss: 1.2026... Generator Loss: 0.6032
Epoch 1/1... Discriminator Loss: 1.1411... Generator Loss: 0.5543
Epoch 1/1... Discriminator Loss: 0.8051... Generator Loss: 0.9116
Epoch 1/1... Discriminator Loss: 0.7127... Generator Loss: 0.9766
Epoch 1/1... Discriminator Loss: 1.0331... Generator Loss: 0.6987
Epoch 1/1... Discriminator Loss: 0.9007... Generator Loss: 0.9740
Epoch 1/1... Discriminator Loss: 0.7898... Generator Loss: 0.8211
Epoch 1/1... Discriminator Loss: 1.4904... Generator Loss: 0.4575
Epoch 1/1... Discriminator Loss: 1.1885... Generator Loss: 0.5600
Epoch 1/1... Discriminator Loss: 1.1116... Generator Loss: 0.7001
Epoch 1/1... Discriminator Loss: 0.9489... Generator Loss: 0.7734
Epoch 1/1... Discriminator Loss: 0.6501... Generator Loss: 1.3654
Epoch 1/1... Discriminator Loss: 0.8012... Generator Loss: 0.8367
Epoch 1/1... Discriminator Loss: 1.1687... Generator Loss: 0.7470
Epoch 1/1... Discriminator Loss: 1.0510... Generator Loss: 0.6111
Epoch 1/1... Discriminator Loss: 0.7529... Generator Loss: 1.1876
Epoch 1/1... Discriminator Loss: 1.1376... Generator Loss: 0.6074
Epoch 1/1... Discriminator Loss: 0.5826... Generator Loss: 1.1377
Epoch 1/1... Discriminator Loss: 0.6977... Generator Loss: 0.9999
Epoch 1/1... Discriminator Loss: 1.2820... Generator Loss: 0.4822
Epoch 1/1... Discriminator Loss: 0.8195... Generator Loss: 0.8197
Epoch 1/1... Discriminator Loss: 0.7637... Generator Loss: 0.9146
Epoch 1/1... Discriminator Loss: 1.2366... Generator Loss: 0.5339
Epoch 1/1... Discriminator Loss: 0.6170... Generator Loss: 1.0134
Epoch 1/1... Discriminator Loss: 0.7621... Generator Loss: 1.0317
Epoch 1/1... Discriminator Loss: 0.9594... Generator Loss: 0.7312
Epoch 1/1... Discriminator Loss: 0.8594... Generator Loss: 0.9007
Epoch 1/1... Discriminator Loss: 0.9818... Generator Loss: 0.7333
Epoch 1/1... Discriminator Loss: 0.6996... Generator Loss: 1.0279
Epoch 1/1... Discriminator Loss: 1.0990... Generator Loss: 0.7713
Epoch 1/1... Discriminator Loss: 0.6341... Generator Loss: 1.2535
Epoch 1/1... Discriminator Loss: 0.8294... Generator Loss: 0.7983
Epoch 1/1... Discriminator Loss: 1.0135... Generator Loss: 0.6125
Epoch 1/1... Discriminator Loss: 0.9460... Generator Loss: 0.6982
Epoch 1/1... Discriminator Loss: 0.8608... Generator Loss: 1.0345
Epoch 1/1... Discriminator Loss: 0.9742... Generator Loss: 0.6213
Epoch 1/1... Discriminator Loss: 0.9111... Generator Loss: 0.8947
Epoch 1/1... Discriminator Loss: 0.8990... Generator Loss: 0.8368
Epoch 1/1... Discriminator Loss: 0.6461... Generator Loss: 0.9604
Epoch 1/1... Discriminator Loss: 0.7293... Generator Loss: 1.0078
Epoch 1/1... Discriminator Loss: 0.7251... Generator Loss: 1.0960
Epoch 1/1... Discriminator Loss: 1.3426... Generator Loss: 0.4818
Epoch 1/1... Discriminator Loss: 1.4343... Generator Loss: 0.4497
Epoch 1/1... Discriminator Loss: 0.8577... Generator Loss: 0.8423
Epoch 1/1... Discriminator Loss: 1.0239... Generator Loss: 0.6879
Epoch 1/1... Discriminator Loss: 0.8445... Generator Loss: 0.8913
Epoch 1/1... Discriminator Loss: 0.7717... Generator Loss: 1.0927
Epoch 1/1... Discriminator Loss: 0.8718... Generator Loss: 0.7966
Epoch 1/1... Discriminator Loss: 0.7036... Generator Loss: 0.9721
Epoch 1/1... Discriminator Loss: 0.5867... Generator Loss: 1.1685
Epoch 1/1... Discriminator Loss: 0.6180... Generator Loss: 1.3685
Epoch 1/1... Discriminator Loss: 0.5513... Generator Loss: 1.2078
Epoch 1/1... Discriminator Loss: 0.8856... Generator Loss: 0.8190
Epoch 1/1... Discriminator Loss: 0.8618... Generator Loss: 0.8436
Epoch 1/1... Discriminator Loss: 0.6280... Generator Loss: 0.9631
Epoch 1/1... Discriminator Loss: 0.7045... Generator Loss: 1.0090
Epoch 1/1... Discriminator Loss: 1.1358... Generator Loss: 0.7015
Epoch 1/1... Discriminator Loss: 0.9945... Generator Loss: 0.6037
Epoch 1/1... Discriminator Loss: 0.6293... Generator Loss: 1.1720
Epoch 1/1... Discriminator Loss: 1.1158... Generator Loss: 0.5542
Epoch 1/1... Discriminator Loss: 0.7079... Generator Loss: 1.0266
Epoch 1/1... Discriminator Loss: 0.7239... Generator Loss: 1.0256
Epoch 1/1... Discriminator Loss: 0.7908... Generator Loss: 0.9978
Epoch 1/1... Discriminator Loss: 0.5587... Generator Loss: 1.6250
Epoch 1/1... Discriminator Loss: 0.3657... Generator Loss: 2.3159
Epoch 1/1... Discriminator Loss: 1.0464... Generator Loss: 0.6342
Epoch 1/1... Discriminator Loss: 0.6639... Generator Loss: 1.1899
Epoch 1/1... Discriminator Loss: 1.0127... Generator Loss: 0.6152
Epoch 1/1... Discriminator Loss: 0.9319... Generator Loss: 0.5982
Epoch 1/1... Discriminator Loss: 0.8689... Generator Loss: 0.8365
Epoch 1/1... Discriminator Loss: 0.5532... Generator Loss: 1.3659
Epoch 1/1... Discriminator Loss: 0.6072... Generator Loss: 1.4209
Epoch 1/1... Discriminator Loss: 0.7824... Generator Loss: 1.2206
Epoch 1/1... Discriminator Loss: 0.6496... Generator Loss: 1.1927
Epoch 1/1... Discriminator Loss: 0.9737... Generator Loss: 0.6346
Epoch 1/1... Discriminator Loss: 0.9142... Generator Loss: 0.9908
Epoch 1/1... Discriminator Loss: 0.7308... Generator Loss: 0.9476
Epoch 1/1... Discriminator Loss: 0.7800... Generator Loss: 1.2538

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

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